|
| Status |
Public on Jun 01, 2023 |
| Title |
30cycles |
| Sample type |
SRA |
| |
|
| Source name |
JJN3 and 5TGM1 cells
|
| Organism |
Homo sapiens |
| Characteristics |
cell line: JJN3 and 5TGM1 cells
treatment: None
|
| Treatment protocol |
RM82 cells were treated with either DMSO control or SGC-CLK1 inhibitor
|
| Growth protocol |
JJN3, 5TGM1 and RM82 cells were cultured in RPMI with 10% FBS and 1% Pen/Strep in a 37oC incubator with 5% CO2.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
RNA from cells was harvested using Trizol reagent. Libraries were constructed using NEBNext® Ultra RNA Library Prep Kit for Illumina®
RNA libraries were prepared for sequencing using standard NEB protocols
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
PromethION |
| |
|
| Description |
PCR_counts.tsv.gz
|
| Data processing |
The data was processed using a custom pipeline 'pipeline_count' written using cgatcore and included within the TallyNNN repository (https://github.com/cribbslab/TallyNNN). Briefly, the polyA associated UMI sequence by searching for the polyA region and reverse complementing the read if it does not appear in the correct orientation. The 30bp UMI is then identified upstream of the SMART primer by pattern matching for GTACTCTGCGTTGATACCACTGCTT. We then corrected for errors or remove the read based the number of UMI errors and then the UMI is added to the read name. Next, the TSO associated UMI is identified using the SMART primer sequence AAGCAGTGGTATCAACGCAGAGTAAT. The 30bp UMI sequence is then corrected for errors or remove the read based the number of UMI errors and then the UMI is added to the read name. Both the TSO and polyA associated UMIs and primer sequences are removed from the read sequence. For transcrip0t level analysis, the fastq file is then mapped against the transcriptome using minimap2 (v2.22) with the following settings: -ax map-ont -p 0.9 --end-bonus 10 -N 3. The resulting sam file was then sorted and indexed using samtools18. A custom script was then used to add the transcript name to the XT tag of the samfile for downstream counting by homotrimer deduplication, UMI-tools or mclUMI. For gene level analysis, the fastq data was mapped using minimap2 using the following setting: -ax splice -k 14 --sam-hit-only --secondary=no --junc-bed. The resulting sam file was then sorted and indexed followed by feature annotation using featurecounts (v2.0.1) using the following settings to generate an annotated bam file: featureCounts -a (gtf) -o (output) -R BAM. This bam file was then used for downstream counting by UMI-tools or mclUMI. The reference transcriptome and genomes used for the analysis were hg38_ensembl98 and mm10_ensembl88. Downstream differential expression was performed using Deseq2.
|
| |
|
| Submission date |
Nov 28, 2022 |
| Last update date |
Jun 01, 2023 |
| Contact name |
Adam Cribbs |
| E-mail(s) |
[email protected]
|
| Organization name |
University of Oxford
|
| Department |
NDORMS
|
| Street address |
Windmill Road
|
| City |
Oxford |
| ZIP/Postal code |
OX37LD |
| Country |
United Kingdom |
| |
|
| Platform ID |
GPL26167 |
| Series (2) |
| GSE218899 |
Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules [RNA-seq] |
| GSE218903 |
Counting and correcting errors within unique molecular identifiers to generate absolute numbers of sequencing molecules |
|
| Relations |
| BioSample |
SAMN31892523 |
| SRA |
SRX18398119 |
